In an era where Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, the need for a strong foundational understanding is more critical than ever. Building a Machine Learning Model from Scratch is designed to guide readers through the core principles and practical techniques required to construct intelligent systems from the ground up. The book begins with an overview of AI and explores essential concepts such as uninformed and informed search strategies, logic-based reasoning, constraint satisfaction, planning, and decision-making in complex environments. Logical frameworks like First-Order Logic, Resolution, and Knowledge Engineering are thoroughly discussed to build robust inference systems. We then transition into probabilistic reasoning with Bayes’ Rule, Bayesian Networks, and Dempster-Shafer Theory, followed by essential learning methods including decision tree construction and knowledge-based learning models. With clear explanations, algorithmic details, and real-world applications, this book serves as a comprehensive resource for students, researchers, and professionals seeking to master AI and machine learning at a fundamental level.